most citedMinimax Hausdorff estimation of density level sets

3 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ME2020

Nonparametric estimation of highest density regions for COVID-19

Paula Saavedra-Nieves

Highest density regions refer to level sets containing points of relatively high density. Their estimation from a random sample, generated from the underlying density, allows to de…

stat.CO2020

On systems of quotas based on bankruptcy with a priori unions: estimating random arrival-style rules

A. Saavedra-Nieves, P. Saavedra-Nieves

This paper addresses a sampling procedure for estimating extensions of the random arrival rule to those bankruptcy situations where there exist a priori unions. It is based on simp…

stat.ME2020

Nonparametric estimation of directional highest density regions

Paula Saavedra-Nieves, Rosa María Crujeiras

Reconstruction of sets from a random sample of points intimately related to them is the goal of set estimation theory. Within this context, a particular problem is the one related…

math.ST2019

Extent of occurrence reconstruction using a new data-driven support estimator

A. Rodríguez-Casal, P. Saavedra-Nieves

Given a random sample of points from some unknown distribution, we propose a new data-driven method for estimating its probability support S. Under the mild assumption that S is r-…

math.ST20193 cited

Minimax Hausdorff estimation of density level sets

Alberto Rodríguez-Casal, Paula Saavedra-Nieves

Given a random sample of points from some unknown density, we propose a data-driven method for estimating density level sets under the r-convexity assumption. This shape condition…